{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellargraph example: Fully directed GraphSAGE on a directed CORA citation network\n",
    "\n",
    "This example shows the application of *directed* GraphSAGE to a *directed* graph, where the in-node and out-node neighbourhoods are separately sampled and have different weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import NetworkX and stellar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n",
    "from stellargraph.layer import DirectedGraphSAGE\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the graph with directed edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\", create_using=nx.DiGraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the features and subject for the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=0.1, test_size=None, stratify=node_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Neural_Networks': 81,\n",
       "         'Case_Based': 30,\n",
       "         'Genetic_Algorithms': 42,\n",
       "         'Reinforcement_Learning': 22,\n",
       "         'Rule_Learning': 18,\n",
       "         'Probabilistic_Methods': 42,\n",
       "         'Theory': 35})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GraphSAGE model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on.\n",
    "\n",
    "Note that the NetworkX graph is *directed*, so we also treat it here as *directed*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarDiGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Directed multigraph\n",
      " Nodes: 2708, Edges: 5429\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5429]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `DirectedGraphSAGENodeGenerator` as we are predicting node attributes with a `DirectedGraphSAGE` model.\n",
    "\n",
    "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer (5 in-nodes and 5 out-nodes), and 4 in the second layer (2 in-nodes and 2 out-nodes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "in_samples = [5, 2]\n",
    "out_samples = [5, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 12 weights for each head node, 10 for each in-node and 10 for each out-node.\n",
    " * The `bias` and `dropout` are internal parameters of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_model = DirectedGraphSAGE(\n",
    "    layer_sizes=[32, 32], generator=generator, bias=False, dropout=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a model to predict the 7 categories using Keras softmax layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = graphsage_model.build()\n",
    "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "6/6 - 3s - loss: 1.9142 - acc: 0.2148 - val_loss: 1.8021 - val_acc: 0.3597\n",
      "Epoch 2/20\n",
      "6/6 - 3s - loss: 1.7340 - acc: 0.4148 - val_loss: 1.6983 - val_acc: 0.3962\n",
      "Epoch 3/20\n",
      "6/6 - 3s - loss: 1.6092 - acc: 0.4481 - val_loss: 1.5983 - val_acc: 0.4594\n",
      "Epoch 4/20\n",
      "6/6 - 3s - loss: 1.4861 - acc: 0.6000 - val_loss: 1.5000 - val_acc: 0.5603\n",
      "Epoch 5/20\n",
      "6/6 - 3s - loss: 1.3530 - acc: 0.7407 - val_loss: 1.4123 - val_acc: 0.6382\n",
      "Epoch 6/20\n",
      "6/6 - 3s - loss: 1.2602 - acc: 0.8296 - val_loss: 1.3452 - val_acc: 0.6514\n",
      "Epoch 7/20\n",
      "6/6 - 3s - loss: 1.1523 - acc: 0.8593 - val_loss: 1.2781 - val_acc: 0.6674\n",
      "Epoch 8/20\n",
      "6/6 - 3s - loss: 1.0599 - acc: 0.8778 - val_loss: 1.2135 - val_acc: 0.7002\n",
      "Epoch 9/20\n",
      "6/6 - 3s - loss: 0.9889 - acc: 0.8741 - val_loss: 1.1637 - val_acc: 0.7071\n",
      "Epoch 10/20\n",
      "6/6 - 3s - loss: 0.8977 - acc: 0.9185 - val_loss: 1.1050 - val_acc: 0.7248\n",
      "Epoch 11/20\n",
      "6/6 - 3s - loss: 0.8580 - acc: 0.9185 - val_loss: 1.0731 - val_acc: 0.7350\n",
      "Epoch 12/20\n",
      "6/6 - 3s - loss: 0.7700 - acc: 0.9333 - val_loss: 1.0384 - val_acc: 0.7428\n",
      "Epoch 13/20\n",
      "6/6 - 3s - loss: 0.6984 - acc: 0.9481 - val_loss: 1.0105 - val_acc: 0.7469\n",
      "Epoch 14/20\n",
      "6/6 - 3s - loss: 0.6847 - acc: 0.9296 - val_loss: 0.9829 - val_acc: 0.7482\n",
      "Epoch 15/20\n",
      "6/6 - 3s - loss: 0.6325 - acc: 0.9519 - val_loss: 0.9517 - val_acc: 0.7486\n",
      "Epoch 16/20\n",
      "6/6 - 3s - loss: 0.5681 - acc: 0.9481 - val_loss: 0.9340 - val_acc: 0.7518\n",
      "Epoch 17/20\n",
      "6/6 - 3s - loss: 0.5449 - acc: 0.9481 - val_loss: 0.9082 - val_acc: 0.7555\n",
      "Epoch 18/20\n",
      "6/6 - 3s - loss: 0.5371 - acc: 0.9556 - val_loss: 0.8947 - val_acc: 0.7531\n",
      "Epoch 19/20\n",
      "6/6 - 3s - loss: 0.5103 - acc: 0.9593 - val_loss: 0.8789 - val_acc: 0.7568\n",
      "Epoch 20/20\n",
      "6/6 - 3s - loss: 0.4554 - acc: 0.9704 - val_loss: 0.8675 - val_acc: 0.7559\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen, epochs=20, validation_data=test_gen, verbose=2, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(history.history.keys())\n",
    "    metrics = metrics[:len(metrics)//2]\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'test'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model we can evaluate on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.8595\n",
      "\tacc: 0.7625\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions themselves for all nodes using another node iterator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_mapper = generator.flow(all_nodes)\n",
    "all_predictions = model.predict_generator(all_mapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31336</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061127</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106406</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37879</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126012</th>\n",
       "      <td>subject=Genetic_Algorithms</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107140</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102850</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31349</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106418</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Predicted                    True\n",
       "31336           subject=Neural_Networks         Neural_Networks\n",
       "1061127           subject=Rule_Learning           Rule_Learning\n",
       "1106406  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012      subject=Genetic_Algorithms   Probabilistic_Methods\n",
       "1107140              subject=Case_Based                  Theory\n",
       "1102850         subject=Neural_Networks         Neural_Networks\n",
       "31349           subject=Neural_Networks         Neural_Networks\n",
       "1106418                  subject=Theory                  Theory"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data[\"subject\"]})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the predictions to the graph, and save as graphml, e.g. for visualisation in [Gephi](https://gephi.org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nid, pred, true in zip(df.index, df[\"Predicted\"], df[\"True\"]):\n",
    "    Gnx.nodes[nid][\"subject\"] = true\n",
    "    Gnx.nodes[nid][\"PREDICTED_subject\"] = pred.split(\"=\")[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also add isTrain and isCorrect node attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nid in train_data.index:\n",
    "    Gnx.nodes[nid][\"isTrain\"] = True\n",
    "\n",
    "for nid in test_data.index:\n",
    "    Gnx.nodes[nid][\"isTrain\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nid in Gnx.nodes():\n",
    "    Gnx.nodes[nid][\"isCorrect\"] = (\n",
    "        Gnx.nodes[nid][\"subject\"] == Gnx.nodes[nid][\"PREDICTED_subject\"]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "Evaluate node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict_generator(all_mapper)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data.index)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_data.index)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that each class of paper is separated into three general regions. This is due to the fact that the GraphSAGE features from the previous layer for the node itself, the aggregated in-neighbours, and the aggregated out-neighbours are currently concatenated in the form `[node, in_agg, out_agg]`.\n",
    "\n",
    "There are four distinct types of directed neighbourhoods, namely: \n",
    "* Both in-neighbours and out-neighbours;\n",
    "* Only in-neighbours – in this case `out_agg` will be zero; \n",
    "* Only out-neighbours – in this case `in_agg` will be zero;\n",
    "* No in-neighbours or out-neighbours – in this case both `in_agg` and `out_agg` will be zero.\n",
    "\n",
    "The fourth case, isolated nodes having no in-neighbours and no out-neighbours, does not occur for the CORA dataset (see the counts below) therefore the CORA dataset consists of the three types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 nodes have no in or out neighbours\n",
      "486 nodes have in but not out neighbours\n",
      "1143 nodes have out but no in neighbours\n",
      "1079 nodes have in and out neighbours\n"
     ]
    }
   ],
   "source": [
    "directed_neigh_type = [\n",
    "    1 * (len(Gnx.in_edges(node)) > 0) + 2 * (len(Gnx.out_edges(node)) > 0)\n",
    "    for node in node_data.index\n",
    "]\n",
    "\n",
    "print(f\"{sum(nt==0 for nt in directed_neigh_type)} nodes have no in or out neighbours\")\n",
    "print(f\"{sum(nt==1 for nt in directed_neigh_type)} nodes have in but not out neighbours\")\n",
    "print(f\"{sum(nt==2 for nt in directed_neigh_type)} nodes have out but no in neighbours\")\n",
    "print(f\"{sum(nt==3 for nt in directed_neigh_type)} nodes have in and out neighbours\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We visualize the clustering of these different directed neighbourhood types below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0], emb_transformed[1], c=directed_neigh_type, cmap=\"jet\", alpha=alpha\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that we can reduce the splitting effect of these different directed neighbourhood types by adding the aggregated in-neighbourhood and out-neighbourhood rather that concatenating them. This feature is planned to be implemented soon."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
